code: def apply_inverse_letterbox_coordinate_transform(
      bboxes, im_w, im_h, letterbox_w, letterbox_h
  ):
      """
      The predicted bounding box coordinates from the model are not according to original image but the pre-processed image. This function transforms the coorinates
      according to original image by applying inverse letterbox co-rdinate trasforms mathematically.
  
      Args:
  
          bboxes:
               The bounding box coordinates predicted from the model.
          im_w :
               Width of original input image.
          im_h :
               Height of original input image.
  
      """
      bboxes = bboxes.copy()
      for bb in bboxes:
          x, y, w, h = bb[:4]
          x1 = (x - (1 - letterbox_w) / 2.0) / letterbox_w * im_w
          y1 = (y - (1 - letterbox_h) / 2.0) / letterbox_h * im_h
          w1 = w * im_w / letterbox_w
          h1 = h * im_h / letterbox_h
          bb[:4] = x1, y1, w1, h1
      return bboxes
  
file_path: nnabla/python/src/nnabla/models/object_detection/utils.py
imports: import numpy
  from nnabla.models.object_detection.draw_utils import DrawBoundingBoxes
  from nnabla.utils.image_utils import imresize
label_desc: Use image node_id 2 for calling a global function with example usage: apply_inverse_letterbox_coordinate_transform(bboxes, im_w, im_h, letterbox_w, letterbox_h) and returns: bboxes